Microseismic events, also known as micro-earthquakes, are produced during hydrocarbon and geothermal fluid production operations. Typically microseismic events are caused by shear-stress release on pre-existing geological structures, such as faults and fractures, due to production/injection induced perturbations to the local earth stress conditions. In some instances, microseismic events may be caused by rock failure through collapse, i.e., compaction, or through hydraulic fracturing. Such induced microseismic events may be induced or triggered by changes in the reservoir, such as depletion, flooding or stimulation, in other words the extraction or injection of fluids. The signals from microseismic events can be detected in the form of elastic waves transmitted from the event location to remote sensors. The recorded signals contain valuable information on the physical processes taking place within a reservoir.
Various microseismic monitoring techniques are known, and it is also known to use microseismic signals to monitor hydraulic fracturing and waste re-injection. The seismic signals from these microseismic events can be detected and located in space using high bandwidth borehole sensors. Microseismic activity has been successfully detected and located in rocks ranging from unconsolidated sands, to chalks to crystalline rocks.
As discussed above, in order to improve the recovery of hydrocarbons from oil and gas wells, the subterranean formations surrounding such wells can be hydraulically fractured. Hydraulic fracturing is used to create small cracks in subsurface formations to allow oil or gas to move toward the well. Formations are fractured by introducing specially engineered fluids at high pressure and high flow rates into the formations through the wellbores. Hydraulic fractures typically extend away from the wellbore 250 to 750 feet in two opposing directions according to the natural stresses within the formation.
Recently, there has been an effort to monitor hydraulic fracturing and produce maps that illustrate where the fractures occur and the extent of the fractures. Current hydraulic fracture monitoring comprises methods of processing seismic event locations by mapping seismic arrival times and polarization information into three-dimensional space through the use of modeled travel times and/or ray paths. Travel time look-up tables may be generated by modeling for a given velocity model.
Typical mapping methods are commonly known as non-linear event location methods and involve the selection and time picking of discreet seismic arrivals for each of multiple seismic detectors and mapping to locate the source of seismic energy. However, to successfully and accurately locate the seismic event, the discrete time picks for each seismic detector need to correspond to the same arrival of either a “P” or “S” wave and be measuring an arrival originating from the same microseismic or seismic event. During a fracture operation, many hundreds of microseismic events may be generated in a short period of time. Current techniques employed in the industry require considerable human intervention to quality control the time picking results.
Microseismic data analysis traditionally makes use of the difference between picked S and P arrival times to compute the distance and depth of the source; azimuthal polarizations are then used for direction. Inversions typically make use of a modified Geiger's method, based on the classical Levenberg-Marcquardt nonlinear least squares method, to determine optimum locations with uncertainties. Rapid grid search approaches have also been proposed. Location methods that require manual event picking are subjective and time consuming and automated picking approaches, while able to handle large volumes of data, often get misled by noisy and complicated data. Most automatic picking algorithms also do not make use of the noise rejection potential of the full receiver array.
More recently waveform-based approaches have been presented. In one instance, results of a source scanning algorithm applied to earthquake data have been shown. In another instance, a characteristic function based on the product of P and S onset energy ratios has been employed to locate events. In yet another case, results of a 2D elastic migration approach have been shown with event locations inferred where P+S focusing occurred. In yet another instance, the results of an acoustic technique using time reversal or diffraction stack focusing of recorded waveforms have been shown.
The present disclosure is directed to overcoming, or at least reducing the effects of, one or more of the shortcomings that are inherent in the prior microseismic data analysis techniques outlined above. In addition, the important problem of model calibration is addressed, as is the issue of source parameter inversion.